Free Using HPE AI and Machine Learnin Exam HPE2-N69 Exam Practice Test

What is one key target vertical (or HPE Machine Learning Development solutions?

• Hospitality
• K-12education
• Retail
• Manufacturing

You want to set up a simple demo Ouster tor HPE Machine learning Development Environment for the open source Determined AI) on a local machine. You plan to use 'del deploy' to set up the cluster. What software must be installed on the machine before you run that command?

• Kubernetes
• PyTorch
• Terralorm
• Docker

Compared to Asynchronous Successive Halving Algorithm (ASHA), what is an advantage of Adaptive ASHA?

• Adaptive ASHA can handle hyperparameters related to neural architecture while ASHA cannot.
• ASHA selects hyperparameter configs entirely at random while Adaptive ASHA clones higher-performing configs.
• Adaptive ASHA can train more trials in certain amount of time, as compared to ASHA.
• Adaptive ASHA tries multiple exploration/exploitation tradeoffs oy running multiple Instances of ASHA.

The 10 agents in 'my-compute-poor nave 8 GPUs each, you want to change an experiment config to run on multiple GPUs at once. What Is a valid setting tor 'resources_per_trial?

• 10
• 24
• 12
• 20

What is a benefit of HPE Machine Learning Development Environment mat tends to resonate with executives?

• It uses a centralized training architecture that is highly efficient.
• It helps DL projects complete faster for a faster ROI.
• It helps companies deploy models and generate revenue.
• It automatically cleans up data to create better end results.

What type of interconnect does HPE Machine learning Development System use for high-speed, agent-to-agent communications?

• Remote Direct Memory Access (RDMA) overconverged Ethernet (RoCE)
• Slingshot
• InfiniBand
• Data Center Bridging (OCB)-enabled Ethernet

A customer is deploying HPE Machine learning Development Environment on on-prem infrastructure. The customer wants to run some experiments on servers with 8 NVIDIA A too GPUs and other experiments on servers with only Z NVIDIA T4 GPUs. What should you recommend?

• Letting the conductor automatically determine which servers to use for each experiment, based on the number of resource slots required
• Deploying two HPE Machine Learning Development Environment clusters, one tor each server type
• Deploying servers with 8 GPUs as agents and using the conductor to run experiments that require only 2 GPUs
• Establishing multiple compute resource pools on the cluster, one tor servers or each type

You are in a directory on your machine with your experiment config file and your model code. You enter this command:det experiment create myfile.yamlYou receive this error:det experiment create: error: the following arguments are required: model_defWhat should you do?

• Re-enter the command with '-m' in which is the code filename.
• Make sure that the myfile.yaml tile includes code tor a PyTorchTrial or TFKerasTrial class.
• Re-enter the command with a period (.) at the end.
• Make sure that you have already logged into the cluster with the 'det login'' command.

A customer is using fair-share scheduling for an HPE Machine Learning Development Environment resource pool. What is one way that users can obtain relatively more resource slots for their important experiments?

• Set the weight to a higher than default value.
• Set the weight to a lower than default value.
• Set the priority to a lower than default value.
• Set the priority to a higher than default value.

You want to set up a simple demo cluster for HPE Machine Learning Development Environment (or the open source Determined Al) on Amazon Web Services (AWS). You plan to use 'det deploy' to set up the cluster. What is one prerequisite?

• installing the NVIDIA Container Toolkit on your local machine
• Manually creating the AWS EC2 instance with a PostgreSQL database
• Recording the name of a valid AWS EC2 keypair
• Adding Amazon Elastic Kubernetes Services (EKS) to your AWS account